We present the Lindblad Protocol, a distributed consensus mechanism grounded in physical verification rather than computational proof-of-work or proof-of-stake. The protocol treats distributed network state as a continuously evolving density operator governed by the Lindblad master equation for open dissipative quantum systems. Trust is established through the Lindblad Cryptography Protocol (LCP), a four-layer physical verification stack: hardware identity via silicon Physical Unclonable Functions (SRAM PUF), cryptographic signing via P-256 ECDSA derived from PUF output, spatiotemporal entropy via the Hybrid Stochastic Chua circuit (HSC), and irreversible consensus via the Lindblad master equation over a dissipative LoRa mesh network. In existing consensus mechanisms, security guarantees are computational and are therefore bounded by adversarial compute resources. LCP anchors security in thermodynamic law: a recorded state transition cannot be reversed without violating the second law of thermodynamics. The protocol simultaneously proves what was signed, when, where, and who, without a trusted third party. Hardware validation on commodity Heltec ESP32-S3 nodes demonstrates SRAM PUF inter-device Hamming distance of 48.60% (intra-device: 0.00%), a 486Ă separation ratio confirming strong uniqueness. A fuzzy extractor based on BCH(255,139,t=15) achieves 86% rock-stable bit selection across 12 power-cycle enrollment, validated on physical hardware with 100% reproduction fidelity. The protocol is deployed on mainnet: a live network of physical hardware nodes has produced over 21,000 blocks across 35,842 epochs, mining over 1,512,000 PYCO tokens via Physical Coherence Verification (PCV-4), with a bridge operating across Arbitrum One and Polygon providing USDT/USDC settlement. The first peer-to-peer transfer between two real users was completed on May 29, 2026.
Open access
3 source records
Physical Unclonable Functions (PUFs) and Hardware Security
Many proof-of-stake protocols finance validator rewards from two sources: transaction fees and a finite reserve of tokens. This creates a dynamic hand-off problem. Early in the life of the system, fees may be too small to fund the target level of security; later, fees may become sufficient. The central question is whether the reserve provides enough runway for the protocol to remain secure until this fee-only region is reached. We study this problem in a discrete-time stochastic model of validator participation. Token price and transaction demand fluctuate over time, while validators choose participation strategically. We solve the validator entry game and derive an exact state-dependent reserve threshold, i.e., the minimal reserve stock necessary and sufficient to sustain a target security level. This threshold separates three regions: infeasibility, reserve-dependent security, and fee-only security. Security fails if the reserve first falls below the state-dependent threshold, and a successful hand-off occurs exactly if the fee-only region is reached before that failure time. We derive stress-test guarantees that convert lower confidence bands for token price and demand into reserve requirements, and obtain explicit failure-probability and expected hand-off-time bounds. Finally, we extend the model to forward-looking validators and derive the Markov participation condition that captures how current participation affects future reserve-funded rewards. The main implication is that reserve policy should not be evaluated by nominal depletion dates or steady-state reward ratios alone. A protocol can have a large nominal reserve and still be close to security failure after adverse price or demand shocks. Conversely, once demand crosses the fee-only threshold, the reserve becomes redundant for security. This paper provides a tractable equilibrium framework for stress-testing this transition.
We study lossy compression of a finite statement source generated in a fixed deductive environment. The source symbols are statements in a knowledge base endowed with a shared proof system, and reconstruction fidelity is measured by preservation of deductive closure rather than by symbolwise equality. Fixing the proof system and a canonical scan order yields a decomposition of the source alphabet into an irredundant core and redundant stored consequences. At zero distortion, each core symbol induces a set of distortion-free reconstructions. In the nonconfusable (disjoint-core) regime, we show that the minimum zero-distortion rate equals the source mass of the core times the entropy of the source conditioned on that core. In the general confusable-core regime, we characterise the exact zero-distortion rate via a hypergraph-entropy quantity induced by jointly realisable core subsets, with a reduction to Korner-style graph entropy under a natural pairwise realisability condition. For reconstruction alphabets contained in the deductive closure of the source knowledge base, we further prove that the full rate-distortion function depends only on the core, so redundant states are invisible to both rate and distortion. Finally, when the decoder is limited to a bounded inference-depth budget (a bounded number of iterations of the immediate-consequence operator), we obtain an exact rate-depth-distortion characterisation. Under an additional order-robustness assumption identifying the chosen core with the order-free essential set, this characterisation interpolates between classical symbolwise compression and unconstrained deductive compression.
Shannon's rate-distortion theory treats source symbols as unstructured labels. When the source is a knowledge base equipped with a logical proof system, a natural fidelity criterion is closure fidelity: a reconstruction is acceptable if it preserves the deductive closure of the original. This paper develops a rate-distortion theory under this criterion. Central to the theory is the irredundant core-a canonical generating set extracted by a fixed-order deletion procedure, from which the full deductive closure can be rederived. We prove that the zero-distortion semantic rate equals a quantity that is strictly below the classical entropy rate whenever the knowledge base contains redundant states. More generally, the full semantic rate-distortion function depends only on the core; redundant states are invisible to both rate and distortion. We derive a semantic source-channel separation theorem showing a semantic leverage phenomenon: under closure fidelity, the required source rate is reduced by an asymptotic leverage factor greater than one, allowing the same knowledge base to be communicated with proportionally fewer channel uses-not by violating Shannon capacity, but because redundant states become free. We also prove a strengthened Fano inequality that exploits core structure. For heterogeneous multi-agent communication, an overlap decomposition gives necessary and sufficient conditions for closure-reliable transmission and identifies a semantic bottleneck in broadcast settings that persists even over noiseless channels. All results are verified on Datalog instances with up to 24,000 base facts.
Chirag Sathish, Arshad Khan, Deepesh Haldankar, Nikhita G ¡ 5 authors
The increasing adoption of telemedicine has amplified concerns regarding the security of patient data, particularly in the context of remote authentication and the growing threat of advanced cyber and quantum-enabled attacks. Traditional telehealth security mechanisms rely on static authentication and cryptographic protections, which fail to adapt to changing risk conditions and provide limited resilience against credential compromise and future quantum threats. This paper proposes TAPQ-Health, a Threat-Adaptive Post-Quantum Authentication Pipeline that dynamically adjusts the strength of authentication and cryptographic hardness in real time based on contextual and behavioral risk. The proposed framework integrates four components a lightweight context-bound zero-knowledge proof authentication mechanism, a federated machine learning-based risk assessment model, threat-triggered escalation to lattice-based post-quantum cryptography with adaptive re-encryption, and decentralized, tamper-evident storage using IPFS. A fully implemented prototype was evaluated using 200 real telemedicine sessions and a large-scale analysis of 1.3 million authentication records. Experimental results demonstrate a mean end-to-end latency of 102.84 ms, 100 percent authentication success, and a 61 percent reduction in cryptographic overhead compared to static post-quantum configurations, while achieving 96 percent risk detection accuracy. These results indicate that threat-adaptive post-quantum authentication can significantly enhance telemedicine security without compromising usability or scalability.
Payment channel networks enable scalable off-chain payments, but their practical deployment remains constrained by a persistent tension among routing efficiency, liquidity visibility, transaction privacy, and settlement security. Existing multipath routing mechanisms can improve payment success under fragmented liquidity, yet they often expose sensitive balance information, leak structural features of payment routes, and enlarge the attack surface for probing, channel exhaustion, and selective forwarding. This paper presents a novel framework, Adaptive Multipath Proofs (AMPs), for privacy protection and security in payment channel networks. The core idea is to bind multipath routing decisions with lightweight zero-knowledge verifiability, allowing intermediate nodes to validate path feasibility, fragment consistency, and settlement constraints without learning exact channel balances, the complete payment amount, or the global route structure. AMP integrates three mechanisms: a hidden-liquidity feasibility proof that supports privacy-preserving route selection, an adaptive payment-splitting strategy that dynamically determines fragment allocation according to network congestion and balance uncertainty, and a proof-coupled settlement guard that enforces atomicity and timeout consistency across all payment fragments. Together, these mechanisms reduce information leakage while preserving robust payment execution under dynamic network conditions. Experimental evaluation on real Lightning Network topologies and synthetic stress scenarios demonstrates that AMP significantly lowers balance disclosure and endpoint inference risk, improves payment completion under skewed liquidity distributions, and introduces only moderate computational and communication overhead. The results indicate that adaptive proof-carrying multipath routing offers a practical and effective direction for building secure, privacy-preserving, and high-success payment channel networks.
We prove that JensenâShannon divergence (JSD) contraction coefficients exhibit universal strict super-tensorization: for every finite channel W with nontrivial contraction 0 < Ρ_JSD(W) < 1, one has Ρ_JSD(Wâ2) > Ρ_JSD(W). The sequence Ρ_n(W) := Ρ_JSD(Wân) is nondecreasing, strictly increases along doubling, and satisfies lim Ρ_n(W) = 1, while for Ρ_JSD(W) â {0, 1} it is identically 0 or 1. This contrasts sharply with the multiplicative tensorization Ρ_f(Wân) = Ρ_f(W)^n enjoyed by operator-convex f-divergences (KL, Ď², squared Hellinger), for which contraction decays exponentially to zero. To our knowledge, this is the first f-divergence for which a universal strict super-tensorization law is established. The proof uses the OrdentlichâPolyanskiy binary edge reduction, expresses the binary JSD SDPI constant as a normalized posterior-variance functional, and shows strict amplification via the law of total variance. Convergence rate is controlled by the Bhattacharyya coefficient: 1 â Ρ_n(W) ⤠2A^n. Numerical verification over 4729 random channels across 26 configurations confirms zero violations. **Update v1.1:** Includes addendum with three targeted clarifications: (1) precise assumptions for binary edge reduction lemma replacing informal "mild regularity conditions," (2) explicit two-case split in the key strictness argument (Lemma 5.2, Step 2), (3) refined table caption for operator-convex divergences.
The fifth-generation (5G) networks are facing critical security challenges in device authenti- cation for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomorphic encryption to protect model gradients, resulting in substantial latency, communication over- head, and the energy consumption impractical for resource-constrained 5G devices. This paper proposes zero-knowledge federated learning (ZK-FL), eliminating homomorphic encryption by enabling devices to prove model correctness without revealing gradients. Our approach integrates zero-knowledge proofs with FL updates, where each device generates where each device generates a proof Proofi = ZK(Gradienti, Hashi), demon- strating computational integrity.Experimental results from 10,000 authentication attempts demonstrate ZK-FL achieves 78.4 ms average authentication latency versus 342.5 ms for homomorphic encryption-based FL (77% reduction), proof sizes of 0.128 KB versus 512 KB (99.97% reduction), and energy consumption of 284.5 mJ versus 6.525 mJ (95% reduc- tion), while maintaining 99.3% authentication success rate with formal privacy guarantees. These results demonstrate ZK-FL enables practical privacy-preserving authentication for massive-scale 5G deployment.
The advent of next-generation networks, epitomized by Sixth-Generation (6G) wireless systems, signifies a paradigm shift from the simplistic goal of connectivity to a complex ecosystem defined by the convergence of the physical, digital, and biological worlds. This transition, characterized by hyper-density, extreme heterogeneity, and the integration of novel paradigms like terahertz (THz) communications, reconfigurable intelligent surfaces (RIS), and non-terrestrial networks (NTN), fundamentally invalidates many of the security assumptions of previous generations. The very characteristics that enable unprecedented data rates, ultra-low latency, and massive machine-type communicationsâsuch as massive Multiple-Input Multiple-Output (MIMO), distributed ledger technologies, and artificial intelligence (AI)-driven network slicingâalso expand the attack surface, introducing novel vulnerabilities ranging from intelligent jamming and eavesdropping in the physical layer to sophisticated adversarial attacks on AI-based network management functions. This article provides a comprehensive exploration of secure transmission techniques designed for this nascent landscape. It moves beyond the traditional paradigm of cryptography-as-an-overlay to advocate for a holistic, interdisciplinary approach where security is embedded as a foundational property across all protocol layers. The discussion commences with a critical re-evaluation of the evolving threat landscape, identifying key vulnerabilities unique to next-generation architectures. Subsequently, it delves into advanced physical layer security (PLS) techniques, demonstrating how the intrinsic randomness of the wireless channel can be leveraged for secret key generation and covert communications, particularly in the context of massive MIMO and THz bands. The narrative then transitions to the cryptographic layer, examining the imperative shift towards post-quantum cryptography (PQC) to counter the looming threat of quantum decryption, alongside the role of blockchain and distributed ledgers in establishing decentralized trust in a network devoid of fixed infrastructure. A significant portion of the article is dedicated to AI-native security, exploring both the potential of AI to create autonomous, self-healing security mechanisms and the critical vulnerabilities introduced by adversarial machine learning. The analysis culminates in an examination of securing the networkâs foundational pillars, including the integrity of network slicing, the resilience of the Radio Access Network (RAN), and the security of non-terrestrial components. This article concludes that the security of next-generation networks is not merely a technical challenge but a foundational requirement for the socio-economic viability of a hyper-connected future, necessitating a continuous, adaptive, and unified security architecture that evolves in lockstep with the network itself.
Attribute-Based Encryption (ABE) enables fine-grained access control over outsourced data, but its key generation process typically requires users to disclose their complete attribute sets, introducing significant privacy risks. Existing privacy-preserving approachesâsuch as those based on zero-knowledge proofs or tightly coupled interactive protocolsâsuffer from limited scalability, high communication costs, and insufficient support for selective attribute disclosure. To address these limitations, we propose a privacy-enhancing key generation protocol guided by the principle ofMinimal Disclosure, which ensures that users disclose only the minimally necessary subset of attributes required for authorization. Our protocol decouples attribute verification from key issuance: users first obtain cryptographically verifiable attribute tokens, and later issue blinded key requests over selectively chosen attributes. This design enables selective disclosure, supports reusable attribute credentials, and enhances user autonomy. To improve scalability, we introduce a lightweight batch verification mechanism that reduces computation and communication overhead for the attribute authority. We prove that our protocol achieves thebindingandhidingproperties under standard cryptographic assumptions, and we formally verify these guarantees in the symbolic model using the ProVerif tool. In addition, we propose two privacy metricsâAttributeInference Gain (AIG) andPrivacy Gain (PG)âalongside an entropy-based analysis to quantify resistance against attribute inference attacks. Experimental results show that our scheme effectively mitigates inference leakage while offering substantial efficiency gains compared to existing schemes.
Ramesh Kumar, Joy Dutta, M. Vamsi, Uma Sankararao Varri ¡ 5 authors
The integration of Artificial Intelligence (AI) into sixth-generation (6G) networks is a foundational requirement for achieving unprecedented performance, but it also introduces a sophisticated threat landscape that legacy security frameworks cannot address. This paper presents a comprehensive review of this dual role of AI, analyzing its potential to both compromise and safeguard future networks. Since AI has the ability to both protect and compromise security and privacy, its implementation with 6G technology may sometimes be a double-edged sword. The primary objective of this survey is to systematically analyze existing research that integrates AI techniques into 6G architectures, focusing on their implications for security and privacy. Among the concerns being investigated is the fundamental privacy and security risk associated with 6G technologies. Therefore, in order to incorporate and confirm this foundational research as a platform for future research, we have developed a review on the specifics of 6G security and privacy. The methodology involves reviewing recent academic and industrial studies related to AI-enabled 6G frameworks, threat models, and defense mechanisms, with an emphasis on how AI contributes to intrusion detection, authentication, and privacy preservation. This paper begins with a historical analysis of previous networking technologies and how they impacted contemporary 6G networking improvements. Therefore, this article discusses extensively the aspects that have rendered 6G technology relevant as well as the ongoing 6G-based projects. In addition, it identifies and critically evaluates key enabling technologies, including distributed ledger technology (DLT/blockchain), physical layer security (PLS), terahertz (THz) communication, quantum computing, visible light communication (VLC), and distributed AI/ML, that underpin secure 6G environments. The paper concludes by summarizing open challenges, future research opportunities, and potential pathways for building trustworthy AI-driven 6G systems.
An open question recently posed by Fawzi and Ferme [IEEE Transactions on Information Theory 2024], asks whether non-signaling (NS) assistance can increase the capacity of a broadcast channel (BC). We answer this question in the affirmative, by showing that for a certainK-receiver BC model, called Coordinated Multipoint broadcast (CoMP BC) that arises naturally in wireless networks, NS-assistance provides multiplicative gains in both capacity and degrees of freedom (DoF), even achievingK-fold improvements in extremal cases. Somewhat surprisingly, this is shown to be true even for 2-receiver broadcast channels that are semi-deterministic and/or degraded. In a CoMP BC,Bsingle-antenna transmitters, supported by a backhaul that allows them to share data, act as oneB-antenna transmitter, to send independent messages toKreceivers, each equipped with a single receive antenna. A fixed and globally known connectivity matrix specifies for each transmit antenna, the subset of receivers that are connected to (have a non-zero channel coefficient to) that antenna. Besides the connectivity, there is no channel state information at the transmitter. The receivers have perfect channel knowledge. We show that NS-assistance has no DoF advantage in a fully connected CoMP BC. The DoF region is fully characterized for a class of connectivity patterns associated with tree graphs, for which the classical sum-DoF value is shown to be the number of leaf nodes, while the NS-assisted sum-DoF value is the total number of all (non-root) nodes. For arbitrary connectivity patterns, the sum-capacity with NS-assistance is bounded above and below by the min-rank and triangle number of the connectivity matrix, respectively, leading to matching bounds in many cases, e.g., if min(B,K) ⤠6. While translations to Gaussian settings are demonstrated, for simplicity most of our results are presented under noise-free, finite-field (Fq) models. Converse proofs for classical DoF are found by adapting the Aligned Images bounds to the finite field model. Converse bounds for NS-assisted DoF/capacity extend the same-marginals property to the BC with NS-assistance available to all parties. Beyond the BC setting, even stronger (unbounded) gains in capacity due to NS-assistance are established for certain âcommunication with side-informationâ settings, such as the fading dirty paper channel.
In 5G/6G networks, Device-to-Device (D2D) Salvage Transmission (ST) ensures communication continuity when Unreachable User Equipment (X) loses access to the base station. However, securing authentication between X and Salvage User Equipment (S) while maintaining privacy presents a challenge. Traditional authentication methods depend on key exchanges or centralised servers, heightening vulnerability to impersonation and replay attacks. This paper proposes a Zero-Knowledge Proof (ZKP)-based authentication protocol that enables lightweight, privacy-preserving authentication without disclosing private credentials. The approach utilises modular exponentiation and cryptographic hashing, allowing X to demonstrate its legitimacy without revealing its private key. Simulation results indicate that the proposed method achieves low authentication latency ( 1â10 ms), minimal communication overhead ( 80 bytes per session), and high scalability under heavy authentication loads. The scheme provides resistance against man-in-the-middle (MITM), impersonation, and replay attacks, making it highly secure for real-time applications.
Advanced Authentication Protocols Security
Wireless Communication Security Techniques
Physical Unclonable Functions (PUFs) and Hardware Security
This work proves a formal impossibility theorem stating that no observable behavioral or biometric signal can serve as a cryptographic secret under standard semantic security notions (IND-CPA / IND-CCA), in any computational model admitting machine learning approximation and side-channel observability. The result holds in classical, post-quantum, and hybrid adversarial models. We further derive strict architectural consequences for biometric authentication, fuzzy extractors, and behavioral identification systems, showing that such signals may only function as zero-knowledge liveness proofs, not as entropy sources for cryptographic key material.
The rapid evolution of $\mathbf{6 G}$ communication systems demands artificial intelligence (AI) solutions that are not only adaptive and explainable but also secure and compliant with international standards. A major challenge lies in achieving transparency and regulatory compliance in federated wireless environments while ensuring data privacy and performance. This research introduces a novel architecture that integrates auditable Federated Generative Adversarial Networks (GANs) with explainability frameworks such as SHAP and LIME, supported by a blockchain layer for immutable audit trails and traceable AI decisions across distributed nodes. Smart contracts are employed to enable dynamic policy enforcement and fine-grained access control. The framework is aligned with 3 GPP, ITU-T, and IEEE standards, making it suitable for compliance-driven deployments. Experimental evaluation demonstrates that the proposed architecture enhances model interpretability by up to $81 \%$, reduces security risks through blockchain-based auditing, and maintains competitive performance in key 6 G use cases such as intelligent beamforming, dynamic spectrum allocation, and edge device authentication. The findings confirm that compliant, explainable, and secure AI can be achieved in next-generation wireless networks without compromising efficiency or user trust.
Mehmet Ali AygĂźl, Hakan Ali ĂÄąrpan, HĂźseyin Arslan
This paper proposes a novel multi-party key generation method that jointly utilizes channel state information (CSI) and blockchain technology to enhance security in distributed systems. The proposed method starts by extracting CSI from wireless channels, leveraging the channelsâ inherent randomness and reciprocity to generate secure key fragments shared among legitimate parties. Then, the key generation process involves several stages, including quantization, reconciliation, and privacy amplification, ensuring that the resulting keys are secure and synchronized across participants. Blockchain technology is then leveraged to securely commit these keys, ensuring that the key agreements are recorded in a decentralized, tamper-resistant ledger. The proposed method effectively combines the physical-layer properties of CSI with the decentralized nature of blockchain, providing robust protection against eavesdropping and tampering attacks. Theoretical analyses and simulation results demonstrate the effectiveness of the proposed method in terms of key mismatch probability and secrecy capacity. Additionally, the randomness of the generated keys by the proposed method is validated using the National Institute of Standards and Technology randomness tests.
Federated Learning (FL) enables resource-constrained nodes in edge intelligence to train a global model using local data under the coordination of a server without the risk of privacy disclosure. Secure aggregation employs security primitives to encrypt and compute local gradients, enhancing the security attributes of vanilla FL. However, server-driven FL faces communication bottlenecks and high trust risks when coordinating large-scale distributed devices, and the existing secure aggregation with input validation schemes can only verify input vectors of lengths that are powers of 2. In this work, we propose VerifyDFL, a distributed secure aggregation protocol with input validation, which enables clients to locally validate the gradients of others within the decentralized federated learning (DFL) paradigm. Specifically, we propose a distributed proof approach based on Springproofs that supports arbitrary-length input validation. Clients locally verify the Lâ and L2 norms of othersâ inputs with a zero-knowledge manner. Furthermore, we employ k-regular graphs to enhance the communication topology of DFL, which guarantees that each client can securely aggregate gradients locally even when corrupted or dropped clients participate in federated training. The security analysis and proofs ensure that VerifyDFL meets the privacy protection requirements of DFL. We conduct real benchmark experiments to show that VerifyDFL optimizes the computational cost by approximately 20% over the state-of-the-art input validation protocols. Additionally, VerifyDFL enforces Lâ and L2 norm correctness verification on encrypted model gradients in edge intelligence.
In the domain of authentication, information leakage which can lead to identity theft represents a significant challenge in the field of cybersecurity. This challenge is particularly relevant in the context of 5 G tactical bubbles, where secure and efficient authentication mechanisms are critical to gain access to sensitive information and communication services. The concept of Zero-Knowledge Proofs, in particular non-interactive proofs, has gained attention in recent years as robust cryptographic methods for privacy-preserving protocols. Zero-knowledge proofs enable users to prove possession of specific knowledge to verifiers without revealing the knowledge itself in a single interaction round. Despite its growing popularity, Zero-Knowledge Proofs have not yet been fully explored within 5 G tactical bubbles. In this paper, we perform a comparative analysis between traditional authentication mechanisms and Zero-Knowledge Proofs-enabled authentications. To this end, we evaluate the feasibility in terms of time and computational complexity and determine whether these advanced authentication protocols can ensure enhanced privacy and security in 5 G tactical bubbles.
Alfonso Egio, Ălvaro Le Monnier, Muhammad Asad, Maxime CompastiĂŠ ¡ 5 authors
As the sixth generation of the cellular network is set to be deployed around 2030, the needs for ubiquitous connectivity and increased resource demand will press for further collaboration between different stakeholders to constitute an efficient and resilient network fabric. In practice, the deployment of multiple network slices in multiple domains is one of the promising approaches to reach this vision. However, from a privacy standpoint, this introduces additional risks for the customers, as a malicious slice may contemplate the impersonation of a legit one for exfiltrating network traffic. It is therefore necessary to proceed with slice identification and authentication to prevent any collaboration with a non-legit network domain while avoiding exchanging sensitive data before authentication. In response to this challenge, this paper presents a privacy-preserving authentication framework for inter-slice communication. The framework integrates Zero-Knowledge Proofs (ZKPs) for privacy-preserving authentication and Public Key Cryptography (PKC) for secure identity management, ensuring that no sensitive information is jeopardized before a slice can be trusted. We expose an implementation prototype and evaluate it in controlled slicing environment, demonstrating its ability to maintain performance under varying operational constraints. Quantitative results highlight the efficiency limited resource consumption of the authentication model, its scalability in distributed environments, and robustness against security threats.
C. Aparna, S. Radha, C. Aarthi, K. M. Karthick Raghunath
ABSTRACT Mobile Ad hoc networks (MANETs) are key for applications in which flexibility and organization are paramount, but the security of such networks entails threats that can exploit the vulnerability of their open architecture, resulting in various attacks. To address such issues, a novel architectural framework is always required. One such framework is introduced, namely, the HoneyFed Secure Architecture (HFSA), which provides the combination of an advanced honey encryption system with federated learningâbased decentralized security to improve the security of MANET. Honey encryption, on the other hand, employs adaptive deception techniques to generate plausible decoy data on decryption failure, employs dynamic key management for tamper resistance, and provides perfect authentication through multiâfactor methods and zeroâknowledge proofs. We found that federated learning offers decentralized model training, where nodes jointly train local models while exchanging progress updates without exposing raw data, enabling 81.4% more detections of emerging threats while preserving data privacy. Using the proposed HFSA approach achieves a 78% protection improvement against attacks and a 71% reduction in unauthorized access. HFSA offers a robust and scalable framework of security that uses continuous learning and adaptation to the vulnerabilities of the MANETs to enhance network resilience.
Mamoon M. Saeed, Rashid A. Saeed, Mohammad Kamrul Hasan, Elmustafa Sayed Ali ¡ 8 authors
After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations.
P. Selvaraj, A. Hyils Sharon Magdalene, Suresh Sankaranarayanan, Alias Muralidharan R. Rengaraj ¡ 7 authors
This work designs a novel algorithm to address the pressing security challenges anticipated in 6G networks. A combination of AES, zero-knowledge proofs, and RSA algorithms offers a robust framework for enhancing data security and privacy in advanced wireless communication systems. AES and RSA, renowned for their encryption capabilities, are integrated for secure data transmission and key exchange processes in 6G networks. Moreover, the incorporation of zero-knowledge proofs adds an additional layer of security, allowing entities to validate their knowledge without compromising sensitive information. Through extensive simulations and analyses, the effectiveness of the proposed algorithm to ensure secure communication within 6G networks is demonstrated. The algorithm is able to reduce security threats and vulnerabilities. This research lays the groundwork for the development of resilient and trustworthy next-generation communication infrastructures. Finally, the integration of AES, RSA, and zero-knowledge proofs presents a favorable approach to strengthen data security in 6G networks, paving the way for more reliable and secure wireless communication technologies in the future.